| DC Field | Value | Language |
|---|
| dc.contributor.author | Bulakh, Vitalii | |
| dc.contributor.author | Kirichenko, Lyudmyla | |
| dc.contributor.author | Radivilova, Tamara | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:05:11Z | - |
| dc.date.available | 2020-06-19T12:05:11Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | Bulakh V. Time Series Classification Based on Fractal Properties / Vitalii Bulakh, Lyudmyla Kirichenko, Tamara Radivilova // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 198–201. — (Dynamic Data Mining & Data Stream Mining). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52490 | - |
| dc.description.abstract | The article considers classification task of fractal
time series by the meta algorithms based on decision trees.
Binomial multiplicative stochastic cascades are used as input
time series. Comparative analysis of the classification
approaches based on different features is carried out. The
results indicate the advantage of the machine learning methods
over the traditional estimating the degree of self-similarity. | |
| dc.format.extent | 198-201 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.relation.uri | https://doi.org/10.1002/sec.1639 | |
| dc.relation.uri | https://arxiv.org/ftp/arxiv/papers/1601/1601.07709.pdf | |
| dc.subject | multifractal time series | |
| dc.subject | binomial stochastic cascade | |
| dc.subject | classification of time series | |
| dc.subject | Hurst exponent | |
| dc.subject | Random Forest | |
| dc.title | Time Series Classification Based on Fractal Properties | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | Kharkiv National University of Radioelectronics | |
| dc.format.pages | 4 | |
| dc.identifier.citationen | Bulakh V. Time Series Classification Based on Fractal Properties / Vitalii Bulakh, Lyudmyla Kirichenko, Tamara Radivilova // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 198–201. — (Dynamic Data Mining & Data Stream Mining). | |
| dc.relation.references | [1] G. Kaur, V. Saxena and J. Gupta, "Detection of TCP targeted high bandwidth attacks using self-similarity", Journal of King Saud University, Computer and Information Sciences, pp. 1-15, 2017. doi: 10.1016/j.jksuci.2017.05.004. | |
| dc.relation.references | [2] R. Deka and D. Bhattacharyya, "Self-similarity based DDoS attack detection using Hurst parameter," Security and Communication Networks, vol. 9, no. 17, pp. 4468-4481, 2016. doi: https://doi.org/10.1002/sec.1639 | |
| dc.relation.references | [3] S. M. Popa and G. M. Manea, "Using Traffic Self-Similarity for Network Anomalies Detection," 20th International Conference on Control Systems and Computer Science, Bucharest, pp. 639-644, 2015. doi: 10.1109/CSCS.2015.89 | |
| dc.relation.references | [4] A. Banerjee, S. Sanyal, T. Guhathakurata, R. Sengupta and D. Ghosh, “Categorization of stringed instruments with multifractal detrended fluctuation analysis”. [Online]. Available: https://arxiv.org/ftp/arxiv/papers/1601/1601.07709.pdf. [Accessed: 20- Mar- 2018]. | |
| dc.relation.references | [5] Ł. Korus and M. Piórek, "Compound method of time series classification," Nonlinear Analysis: Modelling and Control, vol. 20, no. 4, pp. 545-560, 2015. | |
| dc.relation.references | [6] A. Alghawli, and L. Kirichenko, “Multifractal Properties of Bioelectric Signals under Various Physiological States,” Information Content & Processing International Journal, vol. 2, no.2, pp.138-163, 2015. | |
| dc.relation.references | [7] A. Nechiporenko, "Rhinomanometric signal processing for selection of formalized diagnostic criterion in rhinology," Telecommunications and Radio Engineering, vol. 74, no. 14, pp. 1285-1294, 2015. | |
| dc.relation.references | [8] A. Coelho and C. Lima, "Assessing fractal dimension methods as feature extractors for EMG signal classification," Engineering Applications of Artificial Intelligence, vol. 36, pp. 81-98, 2014. | |
| dc.relation.references | [9] S. P. Arjunan, D. K. Kumar and G. R. Naik, "A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines," Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, pp. 4821-4824, 2010. doi: 10.1109/IEMBS.2010.5627902 | |
| dc.relation.references | [10] H. Zhang, P. Chang-Shing, and C. Qingsheng, “An improved algorithm for feature selection using fractal dimension,” 2nd International Workshop on Databases, Documents, and Information Fusion, Karlsruhe, Germany, July 4-5, 2002 pp.1-8. | |
| dc.relation.references | [11] I. Ivanisenko, L. Kirichenko and T. Radivilova, "Investigation of multifractal properties of additive data stream," 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, pp. 305-308, 2016. doi: 10.1109/DSMP.2016.7583564 | |
| dc.relation.references | [12] J. Kantelhardt, S. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde and H. Stanley, "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, vol. 316, no. 1-4, pp. 87-114, 2002. | |
| dc.relation.references | [13] L. Kirichenko, T. Radivilova, and I. Zinkevich, Comparative Analysis of Conversion Series Forecasting in E-commerce Tasks. In: Shakhovska N., Stepashko V. (eds) Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689. Springer, Cham, 2018. | |
| dc.relation.references | [14] L. Kirichenko, T. Radivilova and V. Bulakh, "Generalized approach to Hurst exponent estimating by time series," Informatics Control Measurement in Economy and Environment Protection, vol. 8, no. 1, pp. 28-31, 2018. | |
| dc.relation.references | [15] L. Kirichenko, T. Radivilova and I. Zinkevich, "Forecasting weakly correlated time series in tasks of electronic commerce," 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, pp. 309-312, 2017. | |
| dc.relation.references | [16] L. Breiman, "Bagging predictors", Machine Learning, vol. 24, no. 2, pp. 123-140, 1996. Doi: 10.1023/A:1018054314350 | |
| dc.relation.references | [17] L. Breiman, “Random Forests”, Machine Learning, vol. 45, no. 1, pp. 5-32, 2001. Doi: 10.1023/A:1010933404324 | |
| dc.relation.references | [18] R. H. Riedi. “Multifractal processes,” in Doukhan P., Oppenheim G., Taqqu M. S. (Eds.), Long Range Dependence: Theory and Applications: Birkhuser, 2002, pp.625–715. | |
| dc.relation.references | [19] L. Kirichenko, T. Radivilova and E. Kayali, “Modeling telecommunications traffic using the stochastic multifractal cascade process,” Problems of Computer Intellectualization, pp.55–63, 2012. | |
| dc.relation.referencesen | [1] G. Kaur, V. Saxena and J. Gupta, "Detection of TCP targeted high bandwidth attacks using self-similarity", Journal of King Saud University, Computer and Information Sciences, pp. 1-15, 2017. doi: 10.1016/j.jksuci.2017.05.004. | |
| dc.relation.referencesen | [2] R. Deka and D. Bhattacharyya, "Self-similarity based DDoS attack detection using Hurst parameter," Security and Communication Networks, vol. 9, no. 17, pp. 4468-4481, 2016. doi: https://doi.org/10.1002/sec.1639 | |
| dc.relation.referencesen | [3] S. M. Popa and G. M. Manea, "Using Traffic Self-Similarity for Network Anomalies Detection," 20th International Conference on Control Systems and Computer Science, Bucharest, pp. 639-644, 2015. doi: 10.1109/CSCS.2015.89 | |
| dc.relation.referencesen | [4] A. Banerjee, S. Sanyal, T. Guhathakurata, R. Sengupta and D. Ghosh, "Categorization of stringed instruments with multifractal detrended fluctuation analysis". [Online]. Available: https://arxiv.org/ftp/arxiv/papers/1601/1601.07709.pdf. [Accessed: 20- Mar- 2018]. | |
| dc.relation.referencesen | [5] Ł. Korus and M. Piórek, "Compound method of time series classification," Nonlinear Analysis: Modelling and Control, vol. 20, no. 4, pp. 545-560, 2015. | |
| dc.relation.referencesen | [6] A. Alghawli, and L. Kirichenko, "Multifractal Properties of Bioelectric Signals under Various Physiological States," Information Content & Processing International Journal, vol. 2, no.2, pp.138-163, 2015. | |
| dc.relation.referencesen | [7] A. Nechiporenko, "Rhinomanometric signal processing for selection of formalized diagnostic criterion in rhinology," Telecommunications and Radio Engineering, vol. 74, no. 14, pp. 1285-1294, 2015. | |
| dc.relation.referencesen | [8] A. Coelho and C. Lima, "Assessing fractal dimension methods as feature extractors for EMG signal classification," Engineering Applications of Artificial Intelligence, vol. 36, pp. 81-98, 2014. | |
| dc.relation.referencesen | [9] S. P. Arjunan, D. K. Kumar and G. R. Naik, "A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines," Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, pp. 4821-4824, 2010. doi: 10.1109/IEMBS.2010.5627902 | |
| dc.relation.referencesen | [10] H. Zhang, P. Chang-Shing, and C. Qingsheng, "An improved algorithm for feature selection using fractal dimension," 2nd International Workshop on Databases, Documents, and Information Fusion, Karlsruhe, Germany, July 4-5, 2002 pp.1-8. | |
| dc.relation.referencesen | [11] I. Ivanisenko, L. Kirichenko and T. Radivilova, "Investigation of multifractal properties of additive data stream," 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, pp. 305-308, 2016. doi: 10.1109/DSMP.2016.7583564 | |
| dc.relation.referencesen | [12] J. Kantelhardt, S. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde and H. Stanley, "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, vol. 316, no. 1-4, pp. 87-114, 2002. | |
| dc.relation.referencesen | [13] L. Kirichenko, T. Radivilova, and I. Zinkevich, Comparative Analysis of Conversion Series Forecasting in E-commerce Tasks. In: Shakhovska N., Stepashko V. (eds) Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689. Springer, Cham, 2018. | |
| dc.relation.referencesen | [14] L. Kirichenko, T. Radivilova and V. Bulakh, "Generalized approach to Hurst exponent estimating by time series," Informatics Control Measurement in Economy and Environment Protection, vol. 8, no. 1, pp. 28-31, 2018. | |
| dc.relation.referencesen | [15] L. Kirichenko, T. Radivilova and I. Zinkevich, "Forecasting weakly correlated time series in tasks of electronic commerce," 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, pp. 309-312, 2017. | |
| dc.relation.referencesen | [16] L. Breiman, "Bagging predictors", Machine Learning, vol. 24, no. 2, pp. 123-140, 1996. Doi: 10.1023/A:1018054314350 | |
| dc.relation.referencesen | [17] L. Breiman, "Random Forests", Machine Learning, vol. 45, no. 1, pp. 5-32, 2001. Doi: 10.1023/A:1010933404324 | |
| dc.relation.referencesen | [18] R. H. Riedi. "Multifractal processes," in Doukhan P., Oppenheim G., Taqqu M. S. (Eds.), Long Range Dependence: Theory and Applications: Birkhuser, 2002, pp.625–715. | |
| dc.relation.referencesen | [19] L. Kirichenko, T. Radivilova and E. Kayali, "Modeling telecommunications traffic using the stochastic multifractal cascade process," Problems of Computer Intellectualization, pp.55–63, 2012. | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 198 | |
| dc.citation.epage | 201 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
